Product Promotion Prediction Model Based on Evaluation Information

This paper mainly studies the impact of evaluation information on e-commerce platform on the future of products. Through natural language processing and rating, an evaluation model based on user rating and evaluation is defined to measure product quality. Among them, evaluations are differentiated: review sentiment coefficient (R) and review length (L).The evaluation model is:D=0.3*S+0.7*( 0.3*L+0.7*R). In order to predict the future reputation of products, based on the above evaluation model, time series is used to rank the products studied. Each customer purchases the product through Markov chain model, so as to predict the probability of future word-of-mouth spread of the product. Use TOPSIS method to select monthly sales, stars and comment sentiment coefficient as indicators. The comprehensive measurement method based on text and score is determined to predict whether the product is successfully promoted.

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Monitoring and prediction of drought by Markov chain model based on SPI and new index in Isfahan

International Journal of Hydrology Science and Technology ◽

10.1504/ijhst.2019.102415 ◽

2019 ◽

Vol 9 (4) ◽

pp. 355 ◽

Cited By ~ 1

Author(s):

Saeid Eslamian ◽

Mahsa Jahadi

Keyword(s):

Markov Chain ◽

Markov Chain Model ◽

Chain Model ◽

Model Based

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Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100321 ◽

2020 ◽

Vol 18 ◽

pp. 100321

Author(s):

Abiyot Legesse Kura ◽

Dereje Likisa Beyene

Keyword(s):

Markov Chain ◽

Cellular Automata ◽

Markov Chain Model ◽

Southern Ethiopia ◽

Chain Model ◽

Model Based

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Disaster Forecasting Approach In Indonesia: A Fuzzy Time Series - Markov Chain Model

10.31227/osf.io/xpkn8 ◽

2019 ◽

Author(s):

Rahmad Syah

Keyword(s):

Time Series ◽

Markov Chain ◽

Natural Disasters ◽

Markov Chain Model ◽

Volcanic Eruptions ◽

Fuzzy Time Series ◽

Chain Model ◽

The Past ◽

The Future ◽

Transport Accidents

The concept of Fuzzy Time Series to predict things that will happen based on the data in the past, while Markov Chain assist in estimating the changes that may occur in the future. With methods are used to predict the incidence of natural disasters in the future. From the research that has been done, it appears the change, an increase of each disaster, like a tornado reaches 3%, floods reaches 16%, landslides reaches 7%, transport accidents reached 25% and volcanic eruptions as high as 50%.

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COVID-19 and homelessness in England: a modelling study of the COVID-19 pandemic among people experiencing homelessness, and the impact of a residential intervention to isolate vulnerable people and care for people with symptoms

10.1101/2020.05.04.20079301 ◽

2020 ◽

Cited By ~ 2

Author(s):

Dan Lewer ◽

Isobel Braithwaite ◽

Miriam Bullock ◽

Max T Eyre ◽

Robert W Aldridge ◽

...

Keyword(s):

Critical Care ◽

Success Factors ◽

Hospital Admissions ◽

Markov Chain Model ◽

Severe Disease ◽

Sensitivity Analyses ◽

Homeless Population ◽

Chain Model ◽

Scenario Analyses ◽

The Impact

AbstractBackgroundThere is an ongoing pandemic of the viral respiratory disease COVID-19. People experiencing homelessness are vulnerable to infection and severe disease. Health and housing authorities in England have developed a residential intervention that aims to isolate those vulnerable to severe disease (COVID-PROTECT) and care for people with symptoms (COVID-CARE).MethodsWe used a discrete-time Markov chain model to forecast COVID-19 infections among people experiencing homelessness, given strong containment measures in the general population and some transmission among 35,817 people living in 1,065 hostels, and 11,748 people sleeping rough (the ’do nothing’ scenario). We then estimated demand for beds if those eligible are offered COVID-PROTECT and COVID-CARE. We estimated the reduction in the number of COVID-19 cases, deaths, and hospital admissions that could be achieved by these interventions. We also conducted sensitivity and scenario analyses to identify programme success factors.ResultsIn a ’do nothing’ scenario, we estimate that 34% of the homeless population could get COVID-19 between March and August 2020, with 364 deaths, 4,074 hospital admissions and 572 critical care admissions. In our ’base intervention’ scenario, demand for COVID-PROTECT peaks at 9,934 beds, and demand for COVID-CARE peaks at 1,366 beds. The intervention could reduce transmission by removing symptomatic individuals from the community, and preventing vulnerable individuals from being infected. This could lead to a reduction of 164 deaths, 2,624 hospital admissions, and 248 critical care admissions over this period. Sensitivity analyses showed that the number of deaths is sensitive to transmission of COVID-19 in COVID-PROTECT. If COVID-PROTECT capacity is limited, scenario analyses show the benefit of prioritising people who are vulnerable to severe disease.ConclusionSupportive accommodation can mitigate the impact of the COVID-19 pandemic on the homeless population of England, and reduce the burden on acute hospitals.

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Modeling Internal Movement of Children Born in Hong Kong to Nonlocal Mothers

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155476 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5476

Author(s):

Paul Yip ◽

Mehdi Soleymani ◽

Kam Pui Wat ◽

Edward Pinkney ◽

Kwok Fai Lam

Keyword(s):

Hong Kong ◽

Markov Chain Model ◽

Local Population ◽

Mainland China ◽

Chain Model ◽

Return Rates ◽

Basic Law ◽

The Future ◽

Survey Results ◽

The Government

In Hong Kong, approximately 300,000 children were born to Mainland China couples in the period 1991–2012. According to Basic Law, the mini constitution of Hong Kong Special Administrative Region (SAR) government, these parents do not have residence rights, but their children do. As a result, most of these children have returned to Mainland China with their parents. An important consideration for policymakers is how many of these children (who are now adults in some cases) will return to Hong Kong for good, and when, as this will have a significant impact on social service provision, especially in the education sector, where it will be necessary to ensure there is capacity to meet the additional demand. Prior survey results conducted by the government suggested that more than 50% of these children would return to Hong Kong before age six. It is important to be able to provide a timely projection of the demand into the future. Here, we make use of the immigration records on the actual movement of these children and propose a Markov chain model to estimate their return rates in the future. Our results show that only about 25% of these children would return rather than 50% estimated by the survey. We also find that parents with better educational attainment levels are associated with lower return rates of their children. Timely and relevant social and public policies are needed to prepare for their return to minimize disruption to the local population and promote social harmony for the whole community.

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